{ "item_title" : "Deep Learning From Scratch with Python", "item_author" : [" Mir Hossain "], "item_description" : "Deep Learning From Scratch with PythonBuild Neural Networks Step by Step Without Black BoxesStop treating deep learning as a black box. Learn how modern neural networks actually work by building them yourself from the ground up using only Python, NumPy, and core mathematics.Deep Learning From Scratch with Python is a hands-on guide designed for students, software developers, data scientists, engineers, and AI enthusiasts who want to understand the foundations of deep learning instead of relying solely on high-level frameworks. Rather than hiding the underlying mechanics behind libraries, this book walks you through every major component of a neural network-from a single artificial neuron to convolutional neural networks, recurrent networks, LSTMs, transformers, and modern deep learning architectures.Every concept is explained step by step with clear illustrations, practical examples, and fully worked code. By the end of the book, you will understand not only how to use deep learning models but also why they work.Inside this book, you'll learn how to: - Build neural networks from scratch using Python and NumPy- Master the mathematics behind deep learning, including linear algebra, calculus, and optimization- Implement forward propagation and backpropagation manually- Understand activation functions, loss functions, and gradient descent- Train and optimize deep neural networks without relying on black-box libraries- Build convolutional neural networks (CNNs) for image recognition- Develop recurrent neural networks (RNNs), LSTMs, and GRUs for sequential data- Understand attention mechanisms and transformer architectures- Apply regularization, weight initialization, learning rate scheduling, and modern optimization techniques- Evaluate, debug, optimize, and deploy trained modelsThis comprehensive guide also includes: - 40 structured chapters that build concepts progressively- 12 practical deep learning projects with complete implementations- Numerous worked examples and practice exercises- Visual diagrams that simplify complex topics- Quick-reference appendices covering Python, NumPy, linear algebra, calculus, probability, debugging, and interview preparationWhether you're preparing for a career in artificial intelligence, strengthening your machine learning foundation, or simply curious about how neural networks really work, this book provides the practical knowledge and confidence to build deep learning systems from first principles.If you're ready to move beyond copy-and-paste code and truly understand deep learning, this book is your complete roadmap.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/9/79/818/198/9798181987371_b.jpg", "price_data" : { "retail_price" : "45.00", "online_price" : "45.00", "our_price" : "45.00", "club_price" : "45.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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